I'm betting the truth is somewhere in between, models are only as good as their training data -- so over time if they prune out the bad key/value pairs to increase overall quality and accuracy it should improve vastly improve every model in theory. But the sheer size of the datasets they're using now is 1 trillion+ tokens for the larger models. Microsoft (ugh, I know) is experimenting with the "Phi 2" model which uses significantly less data to train, but focuses primarily on the quality of the dataset itself to have a 2.7 B model compete with a 7B-parameter model.

https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/

In complex benchmarks Phi-2 matches or outperforms models up to 25x larger, thanks to new innovations in model scaling and training data curation.

This is likely where these models are heading to prune out superfluous, and outright incorrect training data.

I think this thread proves they failed in not creating controversy

which could be anything and is guaranteed to upset someone with either answer.

Funny how it only matters with certain answers.

The reason "Why" is because it should become clear that the topic itself is actively censored, which is the possibility the original comment wanted to discard. But I can't force people to see what they don't want to.

it’s just parroting whatever it’s been trained on

If that's your take on training LLMs, then I hope you aren't involved in training them. A lot more effort goes into doing so, including being able to make sure it isn't just "parroting" it. Another thing entirely is to have post-processing that removes answers about particular topics, which is what's happening here.

Not even being able to answer whether Gaza exists is being so lazy that it becomes dystopian. There are plenty of ways LLM can handle controversial topics, and in fact, Google Gemini's LLM does as well, it just was censored before it could get the chance to do so and subsequently refined. This is why other LLMs will win over Google's, because Google doesn't put in the effort. Good thing other LLMs don't adopt your approach on things.